Harleen Sandhu

Sandhu, H. K., Bodda, S. S., Yan, E., Sabharwall, P., & Gupta, A. (2024, March 1). A comparative study on deep learning models for condition monitoring of advanced reactor piping systems. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, Vol. 209. https://doi.org/10.1016/j.ymssp.2023.111091

Sandhu, H. K., Bodda, S. S., & Gupta, A. (2023). [Review of A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities]. ENERGIES, 16(6). https://doi.org/10.3390/en16062628

Sandhu, H. K., Bodda, S. S., Sauers, S., & Gupta, A. (2023). Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks. JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME, 145(4). https://doi.org/10.1115/1.4062462

Sandhu, H. K., Bodda, S. S., & Gupta, A. (2023). Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning. INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING, 201. https://doi.org/10.1016/j.ijpvp.2022.104849

Nie, G.-Y., Bodda, S. S., Sandhu, H. K., Han, K., & Gupta, A. (2022). Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method. SENSORS, 22(18). https://doi.org/10.3390/s22186869